Learning Off-Road Terrain Traversability with Self-Supervisions Only

Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for new circumstances. In this paper, we introduce a method for learning traversability from images that utilizes only self-supervision and no manual labels, enabling it to easily learn traversability in new circumstances. To this end, we first generate self-supervised traversability labels from past driving trajectories by labeling regions traversed by the vehicle as highly traversable. Using the self-supervised labels, we then train a neural network that identifies terrains that are safe to traverse from an image using a one-class classification algorithm. Additionally, we supplement the limitations of self-supervised labels by incorporating methods of self-supervised learning of visual representations. To conduct a comprehensive evaluation, we collect data in a variety of driving environments and perceptual conditions and show that our method produces reliable estimations in various environments. In addition, the experimental results validate that our method outperforms other self-supervised traversability estimation methods and achieves comparable performances with supervised learning methods trained on manually labeled data.Comment: Accepted to IEEE Robotics and Automation Letters. Our video can be found at https://bit.ly/3YdKan

Experimental and statistical investigation of self-consolidating concrete mixture constituents for prestressed bridge girder fabrication

Self-consolidating concrete (SCC) has the potential to increase precast production and quality, especially for production of prestressed concrete (PSC) bridge girders due to its superior workability compared with conventional concrete (CC). To obtain desired fresh and hardened properties for the production of SCC PSC girders, many factors related to material characteristics and mixture proportioning must be considered. An experimental comparison of fresh and hardened properties of SCC mixtures made with different material constituents was conducted in this study. The ultimate objective of this paper is not only to provide an experimental program enabling the investigation of the effect of material constituents on the performance of SCC mixtures but also to gain more knowledge for improved production of SCC PSC girders. The experimental program was established based on technical findings from a literature review and additional input from a survey of several state departments of transportation (DOTs). The mixture constituents used to investigate SCC performance consisted of the type of cement and size and type of coarse aggregate. Testing methods included slump flow, visual stability index (VSI), J-ring, column segregation, and compressive strength. The testing results showed that the type, shape, and size of coarse aggregate have a dominant effect in terms of fresh properties and compressive strength; specifically, mixtures with river gravel had larger spreads than mixtures with crushed limestone. Cement type had the expected effect with mixtures using Type III cement developing higher early strength than those using Type I/II cement. A statistical analysis was performed to determine significant mixture parameters in terms of fresh and hardened properties. It was found that the fine aggregate content was the most significant parameter affecting both fresh and hardened properties\u27 behavior

Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics

In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solving challenging control problems in robotics by learning unknown or partially known robot dynamics. Active exploration, in which a robot directs itself to states that yield the highest information gain, is essential for efficient data collection and minimizing human supervision. Similarly, uncertainty-aware deployment has been a growing concern in robotic control, as uncertain actions informed by the learned model can lead to unstable motions or failure. However, active exploration and uncertainty-aware deployment have been studied independently, and there is limited literature that seamlessly integrates them. This paper presents a unified model-based reinforcement learning framework that bridges these two tasks in the robotics control domain. Our framework uses a probabilistic ensemble neural network for dynamics learning, allowing the quantification of epistemic uncertainty via Jensen-Renyi Divergence. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC, resulting in efficient collection of training data and successful avoidance of uncertain state-action spaces. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.Comment: 2023 Robotics: Science and Systems (RSS). Project page: https://taekyung.me/rss2023-bridgin

A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance

Measuring similarity between patents is an essential step to ensure novelty of innovation. However, a large number of methods of measuring the similarity between patents still rely on manual classification of patents by experts. Another body of research has proposed automated methods; nevertheless, most of it solely focuses on the semantic similarity of patents. In order to tackle these limitations, we propose a hybrid method for automatically measuring the similarity between patents, considering both semantic and technological similarities. We measure the semantic similarity based on patent texts using BERT, calculate the technological similarity with IPC codes using Jaccard similarity, and perform hybridization by assigning weights to the two similarity methods. Our evaluation result demonstrates that the proposed method outperforms the baseline that considers the semantic similarity only

Temperature-dependent ff-electron evolution in CeCoIn5_5 via a comparative infrared study with LaCoIn5_5

We investigated CeCoIn$_5$ and LaCoIn$_5$ single crystals, which have the same HoCoGa$_5$-type tetragonal crystal structure, using infrared spectroscopy. However, while CeCoIn$_5$ has 4$f$ electrons, LaCoIn$_5$ does not. By comparing these two material systems, we extracted the temperature-dependent electronic evolution of the $f$ electrons of CeCoIn$_5$. We observed that the differences caused by the $f$ electrons are more obvious in low-energy optical spectra at low temperatures. We introduced a complex optical resistivity and obtained a magnetic optical resistivity from the difference in the optical resistivity spectra of the two material systems. From the temperature-dependent average magnetic resistivity, we found that the onset temperature of the Kondo effect is much higher than the known onset temperature of Kondo scattering ($\simeq$ 200 K) of CeCoIn$_5$. Based on momentum-dependent hybridization, the periodic Anderson model, and a maximum entropy approach, we obtained the hybridization gap distribution function of CeCoIn$_5$ and found that the resulting gap distribution function of CeCoIn$_5$ was mainly composed of two (small and large) components (or gaps). We assigned the small and large gaps to the in-plane and out-of-plane hybridization gaps, respectively. We expect that our results will provide useful information for understanding the temperature-dependent electronic evolution of $f$-electron systems near Fermi level.Comment: 23 pages, 8 figure

Sustainable Alternative to Structurally Deficient Bridges

Structurally deficient bridges in the United States may be replaced with a viable alternative made with Cross Laminated Timber (CLT). The alternative promotes environmental sustainability, diversified wood production opportunities, and increased public safety and construction efficiency. CLT products' superior strength, durability and sustainability have led to commercialization for building applications, but CLT has never been applied to bridge systems. The ultimate goal of this project is to improve bridge sustainability and performance using CLT products. To achieve this goal, researchers will pursue the following research objectives: 1) conceptualize a new CLT girder bridge system; 2) design and manufacture the full-scale specimen; and 3) investigate structural performance of the bridge system. To succeed, one CLT fabricator, who serves as an industrial collaborator on this project, will provide practical input for the production of the specimen. Further, one graduate student will gain hands-on research experience and real-world solutions. The PI will integrate the findings into SDSU engineering courses, including CEE 792: Bridge Engineering, to introduce students to CLT bridge performance

Direct 2D-to-3D transformation of pen drawings

Pen drawing is a method that allows simple, inexpensive, and intuitive two-dimensional (2D) fabrication. To integrate such advantages of pen drawing in fabricating 3D objects, we developed a 3D fabrication technology that can directly transform pen-drawn 2D precursors into 3D geometries. 2D-to-3D transformation of pen drawings is facilitated by surface tension-driven capillary peeling and floating of dried ink film when the drawing is dipped into an aqueous monomer solution. Selective control of the floating and anchoring parts of a 2D precursor allowed the 2D drawing to transform into the designed 3D structure. The transformed 3D geometry can then be fixed by structural reinforcement using surface-initiated polymerization. By transforming simple pen-drawn 2D structures into complex 3D structures, our approach enables freestyle rapid prototyping via pen drawing, as well as mass production of 3D objects via roll-to-roll processing

Seismic Response and Performance Evaluation of Self-Centering LRB Isolators Installed on the CBF Building under NF Ground Motions

This paper mainly treats the seismic behavior of lead-rubber bearing (LRB) isolation systems with superealstic shape memory alloy (SMA) bending bars functioning as damper and self-centering devices. The conventional LRB isolators that are usually installed at the column bases supply extra flexibility to the centrically braced frame (CBF) building with a view to elongate its vibration period, and thus make a contribution to mitigating seismic acceleration transferred from ground to structure. However, these base isolation systems are somehow susceptible to shear failure due to the lack of lateral resistance. In the construction site, they have been used to be integrated with displacement control dampers additionally withstanding lateral seismic forces. For this motivation, LRB isolation systems equipped with superelastic SMA bending bars, which possess not only excellent energy dissipation but also outstanding recentering capability, are proposed in this study. These reinforced and recentering LRB base isolators are modeled as nonlinear component springs, and then assigned into the bases of 2D frame models used for numerical simulation. Their seismic performance and capacity in the base-isolated frame building can be evaluated through nonlinear dynamic analyses conducted with historic ground motion data. After comparative study with analyses results, it is clearly shown that 2D frame models with proposed LRB isolators generally have smaller maximum displacements than those with conventional LRB isolators. Furthermore, the LRB isolation systems with superelastic SMA bending bars effectively reduce residual displacement as compared to those with steel bending bars because they provide more flexibility and recentering force to the entire building structure

Is Sparse Identification Model Sufficiently Biased?

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Determination of Soil Parameters to Analyze Mechanical Behavior Using Lade's Double-Surface Work-Hardening Model

In this study, Lade's double-surface work-hardening constitutive model was adopted which uses the elasto-plasticity model as a basic conceptual framework. The model can analyze work hardening and work softening of nonlinear stress-strain behavior, and is regarded as superior to other elasto-plasticity constitutive models in terms of estimation. In the double-surface work-hardening constitutive model, 14 soil parameters are needed to estimate soil behaviors. To determine them, laboratory tests—isotropical consolidation test and conventional compression test—were conducted. Determining of soil parameters is highly complicated and time-consuming; randomness cannot be ruled out in determining parameters that are sensitive to stress-strain estimation, and error may occur. For this reason, a linear and nonlinear regression analysis was used to determine soil parameters. In estimation of undrained behavior, the estimated stress-strain behavior based on the two constitutive models largely overlapped with the test results. However, in estimating drained behavior, the outcome of the two models and the test results were mostly the same, but between the two models, the double-surface work-hardening constitutive model had a sharper slope in initial stress state, and a smaller maximum deviatoric stress